Every low-probability, high-consequence adverse incident or catastrophic operational failure at any production or processing facility, such as a chemical plant, fluid-catalytic-cracking units (FCCU) at a petroleum refinery, nuclear energy production plant, or even a biological facility or waste management facility, is preceded by many high-probability, low-consequence events, which may or may not be recognized by alarms or are considered near-misses (Pariyani et al., Ind. Eng. Chem. Res. 49:8062-8079 (2010a); Pariyani et al., 20th European Symposium on Computer Aided Process Engineering (ESCAPE) 28:175-180 (2010b)). Some of these events remain hidden in the background of normal operating conditions. An ideal risk management system at the plant will account for the near-misses, especially those that are hidden, and develop indicators to notify the operators in advance of undesirable incidents that are likely to happen. In particular, such knowledge becomes highly desirable for unmanned plants/facilities.
For example, in the following situations, the public has been harmed by industrial accidents, adverse events, and/or catastrophic failures that could have been avoided with a DRA system. For example, the US government chemical safety board web site (www.csb.gov) is inundated with reports of accidents that took place in the chemical manufacturing facilities in the recent years that cost several lives, as well as property damage. The recurring themes in the outcome of analysis of these accidents are a) the lack of preventive maintenance, and b) the lack of attention to process near-misses. Moreover, every year billions of dollars are lost in the manufacturing industry due to “trips” (unexpected shutdowns due to malfunction of the equipment and/or control systems) at operational plants and facilities. For instance, there have been $6 billion/year losses recorded by US refineries from unexpected shut downs and associated incidents of crude and fluidized catalytic cracking (FCC) units.
An additional condition, which is frequently observed in most manufacturing or processing facilities, is silencing (muting) the alarms that are considered to be nuisance. These are alarms that are activated so often that they are considered to be of such little significance by the operators, that they are regarded as unimportant disturbances resulting from normal operations, so they are turned off or ignored like fire drills in office buildings. But such actions negate the value of the alarm system. For example, at an offshore refinery facility visited in 2011 by the inventors, most of the “low priority” alarms had been silenced. In fact, one of the reasons that the BP off shore accident in Gulf of Mexico in 2010 (where 11 people died and 17 were injured) was not identified in its early stages was because an alarm had been silenced after it had been going off in the middle of the night and awaking the workers.
Most safety activities are reactive and not proactive, and as a result many organizations wait for losses to occur before taking preventative steps to prevent a recurrence. Near miss incidents often precede loss producing events, but are either hidden within process operations and related data or are largely ignored because no injury, damage, or loss actually occurred. Thus, many opportunities to prevent an accident or adverse incident are lost. However, recognizing and reporting near miss incidents, particularly measurable near misses, such as, for example, by alarms in an alarm-monitored plant/facility or by comparative data, can make a major difference to the safety of workers within organizations, and often to the public at large, e.g., in the case of a nuclear-powered facility wherein in a systems failure poses a significantly high amount of risk. History has shown repeatedly that most loss producing events (accidents) were preceded by warnings or near-miss accidents.
Thus there is a need, not met until the present invention, for a “dynamic risk analyzer” (DRA) system that periodically analyzes real time and historic data to assess operational risks and identify near-misses of alarm and non-alarm based process variables, which are hidden as normal operating conditions and to send alert signals and/or reports to identify the hidden risk and to reduce or prevent adverse incidents or failures.